Toxicity of Hydroquinone Report

Introduction

A common ingredient in skin lightening creams is Hydroquinone, this chemical is a well-known inhibitor of tyrosinase which leads to a reduced melanin production in melanocytes. However, hydroquinone is also known to be selectively toxic to melanocytes though the exact TD50 of this chemical on melanocytes has not been reported. The goal of this experiment will be to determine the TD50 of hydroquinone on melanocytes.

To do this an MTT assay will be preformed on the cells. In this MTT assay B16F10 mouse melanoma cells will be plated in a 96 well plate and exposed to a spectrum of concentrations of hydroquinone ranging from 200uM to just 1.5625uM, additional B16F10 cells will be plated at concentrations of 10,000 cells/well down to 500 cells/well with no hydroquinone as a way to determine the final cell counts of the treated cells. Once the cells have been allowed to incubate for 24 hours the media all the cells are in will be replaced by media containing MTT. The cells will then reduce the yellow MTT into the purple formazan which will crystalize at the bottom of the wells. In order to solubilize the crystals the media will be replaced with DMSO and the OD540nm will be measured.

Materials

Material <- c('B16F10 cells','96 well plate' ,'DMEM' ,'hydroquinone' ,'sterile filter' ,'MTT' ,'DMSO')

Amount <- c('1 confluent 10cm plate','1', '100 ml',  '>110mg', '1', '2ml', '2ml')
mat <- data.frame(Material,Amount)

kable(mat,format = 'html') %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))
Material Amount
B16F10 cells 1 confluent 10cm plate
96 well plate 1
DMEM 100 ml
hydroquinone >110mg
sterile filter 1
MTT 2ml
DMSO 2ml

Methods

To perform the MTT assay the plate 10cm of B16F10 cells were trypsinized using standard cell culture procedure. The cells were then counted on a hemocytometer to determine the density of the cells. Once the cells were counted enough DMEM was added dilute the cells to a concentration of 100,000 cells/ ml. 100ul of the cell solution was then added to all wells in columns 4- 6 so that each well had 10,000 cells. Columns 1-3 received a gradient of cells. Rows A, B, C, D, E, F, G, and H received 10,000, 9,000, 8,000, 6,000, 4,000, 2,000, 1,000, and 500 cells respectively. Media was then added to increase the final volume of all wells to 100ul.

After the cells were aliquoted, they were allowed to incubate for 18 hours at 37c and 10% C02. While the cells incubated a 20mM solution of hydroquinone in DMEM was prepared. To do this 110mg of hydroquinone was dissolved into 10 ml of DMEM, this solution was then added to 40ml of DMEM using a sterile filter. After incubating 2ul of the hydroquinone/DMEM solution and 98ul of DMEM were added to wells A4-A6 to create a 200uM solution. 100ul of the media from wells A 3-6 was then transferred to wells B 3-6 this was repeated for all rows until row H, after the media had been added to H the final 100ul from those columns were discarded. This created a gradient from 200uM hydroquinone in row A to 1.5625uM in row H. The cells were then allowed to incubate for another 24 hours.

Once incubated the media was removed from all wells and replaced with 100ul of 0.5mg/ml MTT in DMEM, and the cells were allowed to incubate for 4 hours in order to allow formazan crystals to form, the media was then carefully aspirated being sure not to disturb the crystals and 50 ul of DMSO was added to each well to solubilize the formazan. The plate was then allowed to sit for 5 minutes to ensure full solubilization. The OD540 of the wells was then measured and recorded.

Results

Using a logarithmic model with the equation ’Cell Count=-4578.544*ln(concentration)+39482.942’ it was found that the TD50 of hydroquinone on B16F10 cells was 117.037uM. The p value of the model used was 0.0012462, indicating that at alpha= 0.01 this model is a reasonable predictor of the B16F10 cell count in the presence of a given concentration of hydroquinone.

row<-rep(c('a','b','c','d','e','f','g','h'),6)
column <- c(rep(1,8),
            rep(2,8),
            rep(3,8),
            rep(4,8),
            rep(5,8),
            rep(6,8))
cells_plated<-c(rep(c(10000, 9000, 8000, 6000,4000, 2000, 1000, 500),3), 
                rep(10000,24))

abs_540 <- c(1.152,0.916,0.758,0.342,0.325,0.196,0.173,0.131,
             1.121,0.762,0.789,0.381,0.479,0.280,0.158,0.117,
             1.174,0.802,0.344,0.705,0.510,0.321,0.186,0.115,
             0.563,0.552,0.749,1.006,1.078,1.241,0.879,0.919,
             0.438,0.585,0.371,0.546,0.586,0.963,0.748,0.913,
             0.326,0.438,0.514,0.702,0.746,0.340,0.915,0.542)
  
    


concentration <- c(rep(NA,24))
c <- 400
for (i in 1:8) {
  c <- c/2
  concentration <- c(concentration,c)
}
concentration <- c(concentration,concentration[25:32])
concentration <- c(concentration,concentration[25:32])
 
dat<-data.frame(row,column,cells_plated,abs_540,concentration) %>% 
  mutate(Treatment=case_when(column==1~'control',
                             column==2~'control',
                             column==3~'control',
                             column==4~'hydroquinone',
                             column==5~'hydroquinone',
                             column==6~'hydroquinone'))%>% 
  mutate(cells_attached=cells_plated/2) %>% 
  mutate(final_cells=case_when(Treatment=='control'~cells_attached*2.66667^2))
##########################################################################
dat_2 <- dat %>%
  filter(Treatment=='control') %>% 
  group_by(final_cells) %>% 
  summarise(abs_540=mean(abs_540))

mod1 <- lm(data = dat_2,final_cells~abs_540)
mod <- mod1 %>% tidy()


p <- dat_2 %>%
  ggplot(aes(y=final_cells,x=abs_540))+
  geom_point()+
  geom_smooth(method = 'lm',se=FALSE)+
  labs(title ='Standard for B16F10 cells',y='Cells',x='OD540nm' )+
annotate('text',y=40000,x=0.3,
         label='y= 36181.2x-447.8
           r^2=0.9101')+
  theme_minimal()

ggplotly(p)
kable(mod,format = 'html') %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))
term estimate std.error statistic p.value
(Intercept) -447.8272 2811.571 -0.1592801 0.8786746
abs_540 36181.1664 4642.441 7.7935646 0.0002352

Figure 1: the model for this plot tells us that cells= 36181.2(OD540)-447.8 which will be used to determine how many cells were present in the wells that were treated with hydroquinone.

dat_3 <- dat %>%
  filter(Treatment=='hydroquinone') %>% 
  group_by(concentration) %>% 
  summarise(abs_540=mean(abs_540))

preds <- add_predictions(dat_3,model = mod1)

c_preds<- preds %>% 
  filter(concentration>5)


non_lin <- glm(data=c_preds,
    pred~I(ln(concentration)))




r2 <- 1 - non_lin$deviance/non_lin$null.deviance
 
concentration <- c(7:250)
test <- data.frame(
  concentration)

test2 <- add_predictions(test,model = non_lin)


p2 <- c_preds %>% 
  ggplot(aes(x=concentration,y=pred))+ 
  geom_point()+
  geom_line(data=test2,aes(x=concentration,y=pred))+
  labs(x='Concentration of hydroquinone (uM)',
       y='Number of cells',
       title = 'Toxicity Of hydroquinone')+
  theme_minimal()

ggplotly(p2)
non_lin2 <- non_lin %>% tidy()
kable(non_lin2,format = 'html') %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))
term estimate std.error statistic p.value
(Intercept) 39482.942 2116.2008 18.657465 0.0000486
I(ln(concentration)) -4578.544 563.2947 -8.128151 0.0012462

figure 2: The model for this plot tells us that Cell Count=-4578.544*ln(concentration)+39482.942 from this we can derive that concentration=e^(-0.000218(Cell Count)+8.645) which was used to determine the TD50 of hydroquinone on B16F10 cells. For this plot only concentration of hydroquinone >6uM were used as concntrations lower than that did non seem to impact cell growth.


All data collected

kable(dat,format = 'html') %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))
row column cells_plated abs_540 concentration Treatment cells_attached final_cells
a 1 10000 1.152 NA control 5000 35555.644
b 1 9000 0.916 NA control 4500 32000.080
c 1 8000 0.758 NA control 4000 28444.516
d 1 6000 0.342 NA control 3000 21333.387
e 1 4000 0.325 NA control 2000 14222.258
f 1 2000 0.196 NA control 1000 7111.129
g 1 1000 0.173 NA control 500 3555.564
h 1 500 0.131 NA control 250 1777.782
a 2 10000 1.121 NA control 5000 35555.644
b 2 9000 0.762 NA control 4500 32000.080
c 2 8000 0.789 NA control 4000 28444.516
d 2 6000 0.381 NA control 3000 21333.387
e 2 4000 0.479 NA control 2000 14222.258
f 2 2000 0.280 NA control 1000 7111.129
g 2 1000 0.158 NA control 500 3555.564
h 2 500 0.117 NA control 250 1777.782
a 3 10000 1.174 NA control 5000 35555.644
b 3 9000 0.802 NA control 4500 32000.080
c 3 8000 0.344 NA control 4000 28444.516
d 3 6000 0.705 NA control 3000 21333.387
e 3 4000 0.510 NA control 2000 14222.258
f 3 2000 0.321 NA control 1000 7111.129
g 3 1000 0.186 NA control 500 3555.564
h 3 500 0.115 NA control 250 1777.782
a 4 10000 0.563 200.0000 hydroquinone 5000 NA
b 4 10000 0.552 100.0000 hydroquinone 5000 NA
c 4 10000 0.749 50.0000 hydroquinone 5000 NA
d 4 10000 1.006 25.0000 hydroquinone 5000 NA
e 4 10000 1.078 12.5000 hydroquinone 5000 NA
f 4 10000 1.241 6.2500 hydroquinone 5000 NA
g 4 10000 0.879 3.1250 hydroquinone 5000 NA
h 4 10000 0.919 1.5625 hydroquinone 5000 NA
a 5 10000 0.438 200.0000 hydroquinone 5000 NA
b 5 10000 0.585 100.0000 hydroquinone 5000 NA
c 5 10000 0.371 50.0000 hydroquinone 5000 NA
d 5 10000 0.546 25.0000 hydroquinone 5000 NA
e 5 10000 0.586 12.5000 hydroquinone 5000 NA
f 5 10000 0.963 6.2500 hydroquinone 5000 NA
g 5 10000 0.748 3.1250 hydroquinone 5000 NA
h 5 10000 0.913 1.5625 hydroquinone 5000 NA
a 6 10000 0.326 200.0000 hydroquinone 5000 NA
b 6 10000 0.438 100.0000 hydroquinone 5000 NA
c 6 10000 0.514 50.0000 hydroquinone 5000 NA
d 6 10000 0.702 25.0000 hydroquinone 5000 NA
e 6 10000 0.746 12.5000 hydroquinone 5000 NA
f 6 10000 0.340 6.2500 hydroquinone 5000 NA
g 6 10000 0.915 3.1250 hydroquinone 5000 NA
h 6 10000 0.542 1.5625 hydroquinone 5000 NA

table 1: this table shows all data recorded over the course of the experiment.

Discussion

In this experiment it was determined that the TD50 of hydroquinone on B16F10 was 117.037uM. This TD50 may be important information to know when it comes to using hydroquinone based skin lightening creams. however before this data can be directly applied to skin lightening creams it would first need to be better understood what percent of the hydroquinone reaches the melanocytes under the epidermis.

Additionaly, it is important to note that the cells being used are mouse melanoma cells, future experiments may be needed to find the TD50 of hydroquinone on healthy human melanocytes as it may differ from the results obtained using B16F10 cells.